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首页> 外文期刊>Human-Machine Systems, IEEE Transactions on >Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition
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Dynamic Texture Comparison Using Derivative Sparse Representation: Application to Video-Based Face Recognition

机译：使用导数稀疏表示的动态纹理比较：在基于视频的人脸识别中的应用

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摘要

Video-based face, expression, and scene recognition are fundamental problems in human-machine interaction, especially when there is a short-length video. In this paper, we present a new derivative sparse representation approach for face and texture recognition using short-length videos. First, it builds local linear subspaces of dynamic texture segments by computing spatiotemporal directional derivatives in a cylinder neighborhood within dynamic textures. Unlike traditional methods, a nonbinary texture coding technique is proposed to extract high-order derivatives using continuous circular and cylinder regions to avoid aliasing effects. Then, these local linear subspaces of texture segments are mapped onto a Grassmann manifold via sparse representation. A new joint sparse representation algorithm is developed to establish the correspondences of subspace points on the manifold for measuring the similarity between two dynamic textures. Extensive experiments on the Honda/UCSD, the CMU motion of body, the YouTube, and the DynTex datasets show that the proposed method consistently outperforms the state-of-the-art methods in dynamic texture recognition, and achieved the encouraging highest accuracy reported to date on the challenging YouTube face dataset. The encouraging experimental results show the effectiveness of the proposed method in video-based face recognition in human-machine system applications.
机译：基于视频的面部，表情和场景识别是人机交互中的基本问题，尤其是在视频短时的情况下。在本文中，我们提出了一种使用短视频的人脸和纹理识别新的衍生稀疏表示方法。首先，它通过计算动态纹理内圆柱周围的时空方向导数，建立动态纹理片段的局部线性子空间。与传统方法不同，提出了一种非二进制纹理编码技术，该方法使用连续的圆形和圆柱区域提取高阶导数，以避免混叠效应。然后，通过稀疏表示将纹理片段的这些局部线性子空间映射到Grassmann流形上。开发了一种新的联合稀疏表示算法，以建立流形上子空间点的对应关系，以测量两个动态纹理之间的相似度。在本田/ UCSD，身体的CMU运动，YouTube和DynTex数据集上进行的大量实验表明，该方法在动态纹理识别方面始终优于最新方法，并获得了令人鼓舞的最高准确性。日期在具有挑战性的YouTube面部数据集上。令人鼓舞的实验结果表明，该方法在人机系统应用中基于视频的面部识别中是有效的。

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